Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this paper, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.

The internal defect detection of magnetic tile is extremely significant before mounting. Currently, this task is completely realized by manual operation in the magnetic tile manufacturing industry, which results in inefficiency and diseconomy. In this work, we develop an intelligent system based on the acoustic sound for internal defect detection of magnetic tile to overcome these drawbacks. Due to the non-Gaussian and non-stationary characteristics of the acoustic sound, adopting the single modality of the data for internal defect detection of magnetic tile cannot achieve good accuracy. Therefore, we design a multimodal fusion convolutional neural network (MMFCNN) for internal defect detection of magnetic tile. We train the network in an end-to-end way. Our proposed MMFCNN consists of three blocks, i.e., feature extraction block, feature fusion block and internal defect detection block, whose purposes are to extract features from generated modal data, fuse multimodal feature maps and analyze whether the magnetic tile has internal defects, respectively. Moreover, to realize the information interaction and emphasize more representative information at feature extraction stage, we propose a novel attention mechanism, i.e., cross-attention mechanism. Extensive experimental results demonstrate our proposed MMFCNN is effective for internal defect detection of magnetic tile. Our code is available at https://github.com/Clarkxielf/Multimodal-Fusion-Convolutional- Neural-Network-for-Internal-Defect-Detection-of-Magnetic-Tile.

Optical measurement methods for blade profiles attract lots of interest in industry. Due to the nature of the thin-walled and twisted spatial freeform surfaces of blades, the measurement accuracy would be significantly affected by the accumulated error associated with the geometric accuracy and motion stability of the developed multiview system. To overcome these issues, this article proposes a deep feature interaction network for fine registration of the multiview data. In our network, we design a two-branch structure to integrate a global and a local feature extraction branch to encode point cloud features. Moreover, we propose a feature interaction module to strengthen information association between two point clouds during feature extraction. Next, an attention mechanism is used to fuse matching information between two matching matrices obtained from the global-based and the local-based features. Experimental results demonstrate the feasibility and good practical application prospect of this method.

Developing accurate and efficient measurement systems for blade profiles has always been a vital issue in blade manufacturing. Nowadays, optical-based systems for measuring blade profiles have attracted significant interest due to their high efficiency and flexibility. However, as key steps in optical measurement of blades, system calibration and establishment of measurement datum are normally accomplished based on calibration objects, which may introduce uncertainty and affect the accuracy of the measurement. In this article, we propose a novel method for blade-profile measurement based on blade self-features. A measurement system based on a four-axis platform and laser scanning sensor (LSS) is developed. A calibration method based on the blade datum plane feature is introduced to eliminate the pose error of the system without using additional calibration objects. A feature-matching algorithm based on the covariance matrix of the blade local leading-edge feature is further proposed to establish the measurement datum. The proposed method requires smaller system-motion space and fewer LSS viewpoints, which would improve the efficiency and flexibility of the measurement system. Furthermore, less error accumulation can be achieved using our method, thus, resulting in higher measurement accuracy. The experimental results reveal the feasibility and good practical application prospect of this method.

Currently, optical-based method provides a feasible means for blade reconstruction and measurement. However, as the key step, multiview registration is still heavily reliant on the measurement system's motion stability and geometric accuracy. Moreover, the complex and high-reflective freeform surface of blades may result in both insufficient overlaps and less prominent overlap-area features between adjacent views, making accurate multiview data alignment difficult. Thus, we propose a method to realize accurate 3-D reconstruction and measurement of blade profiles based on a scanning system with a laser sensor and a coarse-to-fine registration strategy. First, coarse alignment of the multiview data is achieved by calibrating the system's rotational axis using the blade datum plane feature, which can provide a good initial value for the fine registration and improve the reconstruction efficiency. Then, a fine registration algorithm based on the dynamic encoding of feature-coordinate fusion information is proposed to refine the coarsely aligned data and reduce the effect of system motion error on registration accuracy. Here, the introduction of fusion information can effectively eliminate the redundant correspondences to continuously optimize the matching probability between multiview data in each iteration, improving registration accuracy and efficiency. Finally, experiments on typical blades and comparison with the other fine registration algorithms demonstrate the accuracy and robustness of the proposed method.